kikeven/zerikai_memory

Эта страница переведена автоматически. Технические детали смотрите в оригинале. Открыть оригинал

36 stars 2 forks Качество 83 Тренд 26MIT

A standalone local-only Python MCP server that gives any IDE persistent, workspace-isolated memory. works with any IDE supporting MCP servers

Обзор

⭐ Bookmark the project: If you use this tool, drop a star to save it to your GitHub profile and track new performance updates. Never lose your AI context again. zerikai_memory provides persistent, workspace-isolated memory for every IDE that is local-first, cost-aware, and instant. It uses deterministic Tree-Sitter code parsing indexing to capture entities and deep code descriptions like functions, classes, and docstrings into a local ChromaDB vector store. Accessed via a local MCP interface to slash token costs while maintaining high-resolution codebase mapping, it retrieves hyper-relevant context on query through L2 and Lexical re-indexing with strict source verification (Entity, File, Line Number, and L2). Designed to pair perfectly with low-cost DeepSeek APIs, it injects structured, highly precise local context instead of dumping raw, massive files, maximizing KV cache hits to radically reduce your active token costs. 💡 Status: Active & Self-Contained.

README

Показан фрагмент оригинального README. Открыть оригинал.

zerikai_memory 🧠

⭐ Bookmark the project: If you use this tool, drop a star to save it to your GitHub profile and track new performance updates.

Never lose your AI context again. zerikai_memory provides persistent, workspace-isolated memory for every IDE that is local-first, cost-aware, and instant. It uses deterministic Tree-Sitter code parsing indexing to capture entities and deep code descriptions like functions, classes, and docstrings into a local ChromaDB vector store. Accessed via a local MCP interface to slash token costs while maintaining high-resolution codebase mapping, it retrieves hyper-relevant context on query through L2 and Lexical re-indexing with strict source verification (Entity, File, Line Number, and L2). Designed to pair perfectly with low-cost DeepSeek APIs, it injects structured, highly precise local context instead of dumping raw, massive files, maximizing KV cache hits to radically reduce your active token costs.

💡Status: Active & Self-Contained. This project is used daily and actively maintained by the author. Pull Requests and Issues are closed to keep maintenance overhead low. It is provided fully functional and ready for production use.

⚠️ Platform Support Notice:

Note: This project is developed and actively maintained on Windows. GitHub Actions verifies that basic installation compiles across Windows, macOS, and Linux, but the maintainer cannot troubleshoot platform-specific runtime errors on Mac or Linux. Community pull requests fixing Mac/Linux bugs are highly welcome!

Открыть полный README на GitHub

Установка

This server does not publish a one-line install command.

Open the repository installation guide Новичок в MCP? Начните с руководства на mcpskill.org →

Конфигурация

{
  "mcpServers": {
    "universal-brain": {
      "command": "C:\\path\\to\\zerikai_memory\\venv\\Scripts\\python.exe",
      "args": ["C:\\path\\to\\zerikai_memory\\main.py"]
    }
  }
}